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Towards Physics-Informed Graph Neural Network-based Computational Electromagnetics

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Bakirtzis, Stefanos 
Fiore, Marco 
Wassell, Ian 


This paper presents a generalizable data-driven computational electromagnetics (CEM) framework leveraging graph neural networks (GNNs). The proposed model supports training and inference for CEM scenarios with different simulation domain sizes and electromagnetic properties, while exploiting the locality of GNNs to achieve reduced complexity and enhanced accuracy. Our results indicate that GNNs can successfully infer the electromagnetic field spatiotemporal evolution for arbitrary simulation domain setups, paving the way for fully-fledged data-driven CEM models.



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2024 IEEE International Symposium on Antennas and Propagation and ITNC-USNC-URSI Radio Science Meeting

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The work of Stefanos Bakirtzis is supported by the Onassis Foundation and the Foundation for Education and European Culture.